IEMS 459: Convex Optimization VIEW ALL COURSE TIMES AND SESSIONS Prerequisites Linear Algebra, Calculus , Real Analysis Description The goal of this course is to investigate in-depth and to develop ...

Course Description This course discusses basic convex analysis (convex sets, functions, and optimization problems), optimization theory (linear, quadratic, semidefinite, and geometric programming; ...

Various non-convex optimization algorithms are thus designed to seek an optimal solution by introducing different constraints, frameworks, and initializations.

These convex optimization algorithms, when applied under the right conditions, come with guarantees of convergence, meaning they are more likely to find the correct answer to the problem.

Rice University computer scientist Anastasios Kyrillidis has won a National Science Foundation CAREER Award to explore the theory and design of non-convex optimization algorithms, an increasingly ...

The portfolio optimization model has limited impact in practice because of estimation issues when applied to real data. To address this, we adapt two machine learning methods, regularization and cross ...

IEMS 458: Convex Optimization VIEW ALL COURSE TIMES AND SESSIONS Prerequisites 450-2 is recommended but not required Description The course will take an in-depth look at the main concepts and ...